A Study on Optimized Prediction Model Selection of Strawberry Production for DPaaS in Distributed Cloud Computing 


Vol. 47,  No. 3, pp. 539-550, Mar.  2022
10.7840/kics.2022.47.3.539


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  Abstract

With the development of IoT devices and diverse of service based on various of data, a data platform that can efficiently manage data is required. Accordingly, existing work for data platform have been researched. It still need to method to boost utilization for using of data platform. In this paper, we design DPaaS based distributed cloud and demonstrate the utilization of the data platform through a smart farm service scenario. According to scenario we evaluate the using the LSTM, convLSTM, and GRU algorithms for prediction of strawberry production. As result of the experiments the RMSE value of the LSTM algorithm was 0.6828, which was selected as the optimal algorithm and applied to the learning model in DPaaS. In the future, we plan to utilize the data platform by improving for convenient user access. It is expected that DPaaS will be used not only data platform research but also related research for smart farm.

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  Cite this article

[IEEE Style]

S. Lee, J. Kim, H. Kim, J. Yoo, A. Son, "A Study on Optimized Prediction Model Selection of Strawberry Production for DPaaS in Distributed Cloud Computing," The Journal of Korean Institute of Communications and Information Sciences, vol. 47, no. 3, pp. 539-550, 2022. DOI: 10.7840/kics.2022.47.3.539.

[ACM Style]

Se-Yun Lee, Junkyeong Kim, Hye-Rin Kim, Jeong-Min Yoo, and A-Young Son. 2022. A Study on Optimized Prediction Model Selection of Strawberry Production for DPaaS in Distributed Cloud Computing. The Journal of Korean Institute of Communications and Information Sciences, 47, 3, (2022), 539-550. DOI: 10.7840/kics.2022.47.3.539.

[KICS Style]

Se-Yun Lee, Junkyeong Kim, Hye-Rin Kim, Jeong-Min Yoo, A-Young Son, "A Study on Optimized Prediction Model Selection of Strawberry Production for DPaaS in Distributed Cloud Computing," The Journal of Korean Institute of Communications and Information Sciences, vol. 47, no. 3, pp. 539-550, 3. 2022. (https://doi.org/10.7840/kics.2022.47.3.539)

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pISSN : 1226 - 4717
eISSN : 2287 - 3880